Table of Contents

Research Background: How does H3K27ac become a key factor in small cell lung cancer classification?

Data Acquisition: How can low-performance devices efficiently download genomic data?

Preprocessing and Analysis: A practical guide from raw data to visualized results.

Preliminary Analysis: Showcasing epigenomic differences across different small cell lung cancer cell lines.

1. Research Background: How does H3K27ac become a key factor in small cell lung cancer classification?

a. Small cell lung cancer (SCLC) is a highly malignant neuroendocrine tumor, accounting for 15% of lung cancer cases. It is characterized by rapid early metastasis, drug resistance, and a low survival rate (5-year survival rate < 7%). Although immunotherapy offers some hope as a first-line treatment, its efficacy remains limited. In recent years, research has found that the epigenetic heterogeneity of SCLC is a core factor driving tumor evolution and therapeutic resistance. Among these, H3K27ac (acetylation of lysine 27 on histone H3), a marker of active enhancers, has become a breakthrough in understanding the heterogeneity of SCLC.

b. Core functions of H3K27ac:

Regulation of key pathways: H3K27ac marks "super enhancers," driving the expression of oncogenic pathways such as Notch, Hedgehog, and neuroendocrine differentiation genes (e.g., ASCL1, NEUROD1).

c. Subtype-specific distribution: Single-cell epigenomics show significant differences in H3K27ac distribution across different SCLC subtypes (A/N/P/Y), directly related to subtype transitions. For example, under chemotherapy pressure, the SCLC-A subtype activates the Wnt/β-catenin pathway by remodeling H3K27ac, acquiring stem cell-like properties and inducing resistance.

d. Therapeutic target: Targeting H3K27ac regulators (e.g., BET inhibitors like JQ1) can disrupt oncogenic enhancer networks and reverse drug resistance. The dynamic changes in H3K27ac are also related to the remodeling of the immune microenvironment, influencing tumor immune escape.

e. Scientific value: The H3K27ac map not only serves as a supplementary biomarker for molecular classification of SCLC but also offers intervention strategies in its regulatory network (such as enhancer inhibitors combined with chemotherapy/immunotherapy) that may break through existing treatment bottlenecks.

2. Data Acquisition: How can low-performance devices efficiently download genomic data?

Step 1: Filter datasets from the GEO database

Use literature search to identify suitable H3K27ac ChIP-seq data (e.g., SCLC sample data) from GEO. Follow the steps below: Click the link https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA760440 to download Metadata and Accession List files.

Download the sample list (SRR_Acc_List.txt) and save it locally. Use Windows PowerShell commands to download data:

# Use sratoolkit to download SRA files
$lines = Get-Content -Path "SRR_Acc_List.txt"
foreach ($line in $lines) {
    prefetch.exe $line -O SCLC_H3K27ac_ChIP_seq/sra/
}
# Convert SRA files to fastq format
$paths = Get-ChildItem -Path SCLC_H3K27ac_ChIP_seq/sra/*.sra -Recurse
foreach ($path in $paths) {
    fasterq-dump.exe --split-3 $path -O SCLC_H3K27ac_ChIP_seq/fastq/
}

Step 2: File transfer and integrity check

Use EAP's built-in tool, Genes on the Cloud Client tools, which supports breakpoint resume functionality, to upload data to the user storage directory on EAP. First, create a new folder in the "My Data" section of the storage management page, then copy the authorization code and paste it into the client to establish a transfer channel.

Use the web tool to check the MD5 value of the files to ensure data integrity and prevent corruption.

3. Preprocessing and Analysis: A Practical Guide from Raw Data to Visualized Results

Preprocessing Workflow:

Quality Control:

Use FastQC to check the quality of the fastq files, and Trim_galore to remove low-quality sequences.

Alignment and Peak Detection:

Use Bowtie to align reads to the reference genome, and MACS for ChIP-seq peak analysis to identify H3K27ac enriched regions. Calculate the distribution of reads near the transcription start site (TSS).

Functional Annotation:

Motif Enrichment:

Use Homer to perform motif enrichment analysis.

Peak Annotation:

Annotate peaks to genomic elements, including promoters, exons, introns, and intergenic regions. Once the analysis is complete, the report and analysis result files are generated.

Obtaining Reads Count Matrix:

Files such as proximal_peak_regions_2000bp.txt, distal_peak_regions_2000bp.txt, and NA_profile_bins.xls are generated. These files serve as inputs for downstream analysis.

Select the ATAC-seq/ChIP-seq analysis pipeline-v3 in EAP and input the corresponding parameters, including the directory of the uploaded files, output directory, sequencing mode, and genome version for alignment.

Once the analysis is complete, you will obtain the analysis report and result files.

4. Preliminary Analysis: Showcasing Epigenomic Differences Across Different Small Cell Lung Cancer Cell Lines

Download proximal_peak_regions_2000bp.txt and distal_peak_regions_2000bp.txt, then apply visualization analysis tools to interpret the differences between various cell lines.

PCA results show distinct H3K27ac modification profiles between different SCLC subtypes.

Note: metadata.csv Template